Adaptive fuzzy control systems with dynamic structure

被引:6
作者
Chen, Chaio-Shiung [1 ]
机构
[1] Da Yeh Univ, Dept Mech & Automat Engn, Da Tsuen 515, Changhua, Taiwan
关键词
process control; adaptive fuzzy control; nonlinear system; stability;
D O I
10.1080/00207720701748224
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article proposes a novel fuzzy system, referred to as a dynamic structure fuzzy system, to address tracking control problems for unknown nonlinear dynamical systems. The fuzzy system is employed to reconstruct the unknown nonlinearities of dynamic systems. In the dynamic structure fuzzy system, the number of fuzzy rules can be either increased or decreased over time based on the required approximation accuracy. The advantage of the dynamic structure fuzzy system is that a suitable-sized fuzzy system can be found to avoid overfitting or underfitting data sets. By using Gaussian radial basis function (GRBF) as a membership function, adaptation laws are presented for tuning all parameters of the parameterized fuzzy system, including the output weights, the widths and the centers of the GRBF's. Global boundedness of the overall control scheme is guaranteed in the sense of Lyapunov. The tracking error converges to the required precision through the adaptive control scheme derived by the Lyapunov synthesis approach. Simulations performed on an underwater vehicle system demonstrate the effectiveness of our scheme.
引用
收藏
页码:163 / 172
页数:10
相关论文
共 18 条
[1]   Guaranteed tracking and regulatory performance of nonlinear dynamic systems using fuzzy neural networks [J].
Behera, L ;
Anand, KK .
IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS, 1999, 146 (05) :484-491
[2]  
Chang YC, 2001, IEEE T FUZZY SYST, V9, P278, DOI 10.1109/91.919249
[3]   Robust model reference adaptive control of nonlinear systems using fuzzy systems [J].
Chen, CS ;
Chen, WL .
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 1996, 27 (12) :1435-1442
[4]   A sampled-data iterative learning control using fuzzy network design [J].
Chien, CJ .
INTERNATIONAL JOURNAL OF CONTROL, 2000, 73 (10) :902-913
[5]   A PARAMETER-ESTIMATION PERSPECTIVE OF CONTINUOUS-TIME MODEL-REFERENCE ADAPTIVE-CONTROL [J].
GOODWIN, GC ;
MAYNE, DQ .
AUTOMATICA, 1987, 23 (01) :57-70
[6]   Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators [J].
Han, H ;
Su, CY ;
Stepanenko, Y .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2001, 9 (02) :315-323
[7]   NEURAL-NETWORK-BASED FUZZY-LOGIC CONTROL AND DECISION SYSTEM [J].
LIN, CT ;
LEE, CSG .
IEEE TRANSACTIONS ON COMPUTERS, 1991, 40 (12) :1320-1336
[8]   Variable neural networks for adaptive control of nonlinear systems [J].
Liu, GPP ;
Kadirkamanathan, V ;
Billings, SA .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 1999, 29 (01) :34-43
[9]   A SELF-TUNING FUZZY CONTROLLER [J].
MAEDA, M ;
MURAKAMI, S .
FUZZY SETS AND SYSTEMS, 1992, 51 (01) :29-40
[10]   A NEW ADAPTIVE LAW FOR ROBUST ADAPTATION WITHOUT PERSISTENT EXCITATION [J].
NARENDRA, KS ;
ANNASWAMY, AM .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1987, 32 (02) :134-145